Computer-aided design decision support: interfacing knowledge and information

Computer-aided design decision support has proved to be an elusive and intangible project for many researchers as they seek to encapsulate information and knowledge-based systems as useful multifunctional data structures. Definitions of ‘knowledge', ‘information', ‘facts', and ‘data' become semantic footballs in the struggle to identify what designers actually do, and what level of support would suit them best, and how that support might be offered. The Construction Primer is a database-drivable interactive multimedia environment that provides readily updated access to many levels of information aimed to suit students and practitioners alike. This is hardly a novelty in itself. The innovative interface and metadata structures, however, combine with the willingness of national building control legislators, standards authorities, materials producers, building research organisations, and specification services to make the Construction Primer a versatile design decision support vehicle. It is both compatible with most working methodologies while remaining reasonably future-proof. This paper describes the structure of the project and highlights the importance of sound planning and strict adhesion to library-standard metadata protocols as a means to avoid the support becoming too specific or too paradigmatic.

Visual feedback control of a robot in an unknown environment (learning control using neural networks)

In this paper, a visual feedback control approach based on neural networks is presented for a robot with a camera installed on its end-effector to trace an object in an unknown environment. First, the one-to-one mapping relations between the image feature domain of the object to the joint angle domain of the robot are derived. Second, a method is proposed to generate a desired trajectory of the robot by measuring the image feature parameters of the object. Third, a multilayer neural network is used for off-line learning of the mapping relations so as to produce on-line the reference inputs for the robot. Fourth, a learning controller based on a multilayer neural network is designed for realizing the visual feedback control of the robot. Last, the effectiveness of the present approach is verified by tracing a curved line using a 6-degrees-of-freedom robot with a CCD camera installed on its end-effector. The present approach does not necessitate the tedious calibration of the CCD camera and the complicated coordinate transformations.

Speed control and policing in a cellular mobile network: speedNet

In this paper, we describe SpeedNet, a GSM network variant which resembles an ad hoc wireless mobile network where base stations keep track of the velocities of mobile users (cars). SpeedNet is intended to track mobile users and their speed passively for both speed policing and control of traffic. The speed of the vehicle is controlled in a speed critical zone by means of an electro-mechanical control system, suitably referred to as VVLS (Vehicular Velocity Limiting System). VVLS is mounted on the vehicle and responds to the command signals generated by the base station. It also determines the next base station to handoff, in order to improve the connection reliability and bandwidth efficiency of the underlying network. Robust Extended Kalman Filter (REKF) is used as a passive velocity estimator of the mobile user with the widely used proportional and integral controller speed control. We demonstrate through simulation and analysis that our prediction algorithm can successfully estimate the mobile user’s velocity with low system complexity as it requires two closest mobile base station measurements and also it is robust against system uncertainties due to the inherent deterministic nature in the mobility model.

Smart cluster bombs - control of multi-agent systems for military applications

This paper introduces an aggregation algorithm for airborne swarming guided weapon systems, which can aggregate munitions into a given shape while reaching the surface. The algorithm uses an artificial force based controller to navigate the members of the swarm into the desired geographical position and evenly distribute them inside the shape. Inter-member repulsion forces are used to avoid collisions among members, which is crucial for a weapon deployment system. Moreover, a lower bound for the release height was obtained which guarantee convergence of the complete weapon system into the target area. The proposed swarming guided weapon system was tested using computer simulations.

Development of a new versatile computer controlled electrochemical/ESR data acquisition system

A new versatile computer controlled electrochemlcal/ESR data acquisition system has been developed for the Investigation of short-lived radicals with life-times of 20 milliseconds and greater, Different computer programs have been developed to monitor the decay of radicals; over hours or minutes, seconds or milliseconds. Signal averaging and Fourier smoothing is employed in order to improve the signal to noise ratio. Two microcomputers are used to control the system, one home-made computer containing the M6800 chip which controls the magnetic field, and an IBM PC XT which controls the electrochemistry and the data acquisition. The computer programs are written in Fortran and C, and call machine language subroutines, The system functions by having the radical generated by an electrochemical pulse: after or during the pulse the ESR data are collected. Decaying radicals which have half-lives of seconds or greater have their spectra collected in the magnetic field domain, which can be swept as fast as 200 Gauss per second. The decay of the radicals in the millisecond region is monitored by time-resolved ESR: a technique in which data is collected in both the time domain and in the magnetic field domain. Previously, time-resolved ESR has been used (without field modulation) to investigate ultra-short-lived species with life-times in the region of only a few microseconds. The application of the data acquisition system to chemical systems is illustrated. This is the first time a computer controlled system whereby the radical is generated by electrochemical means and subsequently the ESR data collected, has been developed.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Modeling data processing activity to assess the internal control function

This thesis describes research that was conducted into the potential of modeling the activities of the Data Processing Department as an aid to the computer auditor. A methodology is composed to aid in the evaluation of the Internal Controls, particularly the General Controls relative to computer processing. Consisting of three major components, the methodology enables the auditor to model the presumed activities of the Data Processing Department against the actual activities, as recorded on the Operating System Log. The first component of the methodology is the construction and loading of a model of the presumed activities of the Data Processing Department from its verbal, scheduled, and reported activities. The second component is the generation of a description of the actual activities of the Data Processing Department from the information recorded on the Operating System Log. This is effected by reducing the Operating System Log to the format described by the Standard Audit File concept. Finally, the third component in the methodology is the modeling process itself. This is in fact a new analysis technique proposed for use by the EDP auditor. The modeling process is composed of software that compares the model developed and loaded in the first component, with the description of actual activity as collated by the second component. Results from this comparison are then reviewed by the auditor, who determines if they adequately depict the situation, or whether the models description as specified in the first component requires to be altered, and the modeling process re-initiated. In conducting the research, information and data from a production installation was used. Use of the ‘real-world’ input proved both the feasibility of developing a model of the reported activities of the Data Processing Department, and the adequacy of the operating system log as a source of information to report the departments actual activities. Additionally, it enabled the involvement and comment of practicing auditors. The research involved analysis of the effect of EDP on the audit process, structure of the EDP audit process, data reduction, data structures, model formalization, and model processing software. Additionally, the Standard Audit File concept was verified through its use by practising auditors, and expanded by the development of an indexed data structure, which enabled its analysis to be conducted interactively. Results from the trial implementation of the research software and methodology at a production installation confirmed the research hypothesis that the activities of the Data Processing Department could be modelled, and that there are substantial benefits from the EDP auditor in analysing this process. The research in fact provides a new source of information, and develops a new analysis technique for the EDP auditor. It demonstrates the utilization of computer technology to monitor itself for the audit function, and reasserts auditor independence by providing access to technical detail describing the processing activities of the computer.

Haptic control methodologies for rover teleoperation in non-deterministic remote environments

Haptic teleoperation allows human operators to interact with a remote mobile robot using their haptic sensory modality. This research introduces new haptic control methodologies allowing the teleoperator to overcome the limitations of existing techniques, ultimately facilitating improved mobile robotic control for the exploration of hazardous and remote environments.

Human-computer interaction in ubiquitous computing environments

Purpose – The purpose of this paper is to explore characteristics of human-computer interaction when the human body and its movements become input for interaction and interface control in pervasive computing settings.

Design/methodology/approach – The paper quantifies the performance of human movement based on Fitt's Law and discusses some of the human factors and technical considerations that arise in trying to use human body movements as an input medium.

Findings – The paper finds that new interaction technologies utilising human movements may provide more flexible, naturalistic interfaces and support the ubiquitous or pervasive computing paradigm.

Practical implications – In pervasive computing environments the challenge is to create intuitive and user-friendly interfaces. Application domains that may utilize human body movements as input are surveyed here and the paper addresses issues such as culture, privacy, security and ethics raised by movement of a user's body-based interaction styles.

Originality/value – The paper describes the utilization of human body movements as input for interaction and interface control in pervasive computing settings.

Simulation-based learning for control of complex conveyor networks

This research investigated the problem of path planning in complex conveyor networks. A reinforcement learning approach was applied to derive a control strategy for routing traffic. The derived strategy was verified in real world systems and was found to improve network performance by prioritising traffic flows and balancing network load.

An empirical examination of feedback : user control and performance in a hapto-audio-visual training environment

Utilising advanced technologies, such as virtual environments (VEs), is of importance to training and education. The need to develop and effectively apply interactive, immersive 3D VEs continues to grow. As with any emerging technology, user acceptance of new software and hardware devices is often difficult to measure and guidelines to introduce and ensure adequate and correct usage of such technologies are lacking. It is therefore imperative to obtain a solid understanding of the important elements that play a role in effective learning through VEs. In particular, 3D VEs may present unusual and varied interaction and adoption considerations. The major contribution of this study is to investigate a complex set of interrelated factors in the relatively new sphere of VEs for training and education. Although many of the factors appears to be important from past research, researcher have not explicitly studied a comprehensive set of inter-dependant, empirically validated factors in order to understand how VEs aid complex procedural knowledge and motor skill learning. By integrating theory from research on training, human computer interaction (HCI), ergonomics and cognitive psychology, this research proposes and validates a model that contributes to application-specific VE efficacy formation. The findings of this study show visual feedback has a significant effect on performance. For tactile/force feedback and auditory feedback, no significant effect were found. For satisfaction, user control is salient for performance. Other factors such as interactivity and system comfort, as well as level of task difficulty, also showed effects on performance.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.